BM3D Denoising Algorithm with Adaptive Block-Match Thresholds

2012 ◽  
Vol 229-231 ◽  
pp. 1715-1720 ◽  
Author(s):  
You Sai Zhang ◽  
Shu Jin Zhu ◽  
Yuan Jiang Li
Keyword(s):  
Similarity Index ◽  
Block Size ◽  
Structural Similarity ◽  
Block Matching ◽  
Natural Image ◽  
Noisy Image ◽  
Nonlocal Means ◽  
Good Improvement ◽  
High Degree ◽  
The Relationship

A number of image filtering algorithms based on nonlocal means have been proposed in recent years which take advantage of the high degree of redundancy of any natural image. The block-matching with 3D transform domain collaborative filtering (BM3D) proposed in [1] achieves excellent performance in image denoising. But the choice of shrinkage operator in block-matching step is not discussed, only given the threshold by experience in its related papers. In this work, we introduce an improved version of BM3D with adaptive block-match thresholds. The proposed method firstly seeks the relationship between the Structural Similarity index (SSIM) [2] and match distance in blocks and obtains the data with fine SSIM values. Then, compute the Noise level and Gradient values in blocks of the same block size. Finally, surface fitting is adopted to get a formula which applies weak thresholds for flat blocks and strong thresholds for detail blocks. Experiment results are given to demonstrate the same class of denoising performance with less time-consuming to slightly noisy image and good improvement in denoising performance to seriously noisy image.

scholarly journals Image Denoising Using a Novel Deep Generative Network with Multiple Target Images and Adaptive Termination Condition

2021 ◽  
Vol 11 (11) ◽  
pp. 4803
Author(s):  
Shiming Chen ◽  
Shaoping Xu ◽  
Xiaoguo Chen ◽  
Fen Li
Keyword(s):  
Image Denoising ◽  
Similarity Index ◽  
Structural Similarity ◽  
Superior Performance ◽  
Natural Image ◽  
Noisy Image ◽  
Multiple Target ◽  
Convolutional Networks ◽  
Training Images ◽  
Image Priors

Image denoising, a classic ill-posed problem, aims to recover a latent image from a noisy measurement. Over the past few decades, a considerable number of denoising methods have been studied extensively. Among these methods, supervised deep convolutional networks have garnered increasing attention, and their superior performance is attributed to their capability to learn realistic image priors from a large amount of paired noisy and clean images. However, if the image to be denoised is significantly different from the training images, it could lead to inferior results, and the networks may even produce hallucinations by using inappropriate image priors to handle an unseen noisy image. Recently, deep image prior (DIP) was proposed, and it overcame this drawback to some extent. The structure of the DIP generator network is capable of capturing the low-level statistics of a natural image using an unsupervised method with no training images other than the image itself. Compared with a supervised denoising model, the unsupervised DIP is more flexible when processing image content that must be denoised. Nevertheless, the denoising performance of DIP is usually inferior to the current supervised learning-based methods using deep convolutional networks, and it is susceptible to the over-fitting problem. To solve these problems, we propose a novel deep generative network with multiple target images and an adaptive termination condition. Specifically, we utilized mainstream denoising methods to generate two clear target images to be used with the original noisy image, enabling better guidance during the convergence process and improving the convergence speed. Moreover, we adopted the noise level estimation (NLE) technique to set a more reasonable adaptive termination condition, which can effectively solve the problem of over-fitting. Extensive experiments demonstrated that, according to the denoising results, the proposed approach significantly outperforms the original DIP method in tests on different databases. Specifically, the average peak signal-to-noise ratio (PSNR) performance of our proposed method on four databases at different noise levels is increased by 1.90 to 4.86 dB compared to the original DIP method. Moreover, our method achieves superior performance against state-of-the-art methods in terms of popular metrics, which include the structural similarity index (SSIM) and feature similarity index measurement (FSIM). Thus, the proposed method lays a good foundation for subsequent image processing tasks, such as target detection and super-resolution.

scholarly journals Image Denoising in Spatial and Transform Domains

2021 ◽  
Author(s):  
Mina Sharifymoghaddam
Keyword(s):  
Image Denoising ◽  
Signal To Noise Ratio ◽  
Similarity Index ◽  
Weighting Scheme ◽  
Block Matching ◽  
Hard Thresholding ◽  
Weighted Version ◽  
Nonlocal Means ◽  
Processing Step ◽  
Index Measure

Image denoising is an inseparable pre-processing step of many image processing algorithms. Two mostly used image denoising algorithms are Nonlocal Means (NLM) and Block Matching and 3D Transform Domain Collaborative Filtering (BM3D). While BM3D outperforms NLM on variety of natural images, NLM is usually preferred when the algorithm complexity is an issue. In this thesis, we suggest modified version of these two methods that improve the performance of the original approaches. The conventional NLM uses weighted version of all patches in a search neighbourhood to denoise the center patch. However, it can include some dissimilar patches. Our first contribution, denoted by Similarity Validation Based Nonlocal Means (NLM-SVB), eliminates some of those unnecessary dissimilar patches in order to improve the performance of the algorithm. We propose a hard thresholding pre-processing step based on the exact distribution of distances of similar patches. Consequently, our method eliminates about 60% of dissimilar patches and improves NLM in terms of Peak Signal to Noise Ratio (PSNR) and Stracuteral Similarity Index Measure (SSIM). Our second contribution, denoted by Probabilistic Weighting BM3D (PW-BM3D), is the result of our thorough study of BM3D. BM3D consists of two main steps. One is finding a basic estimate of the noiseless image by hard thresholding coefficients. The second one is using this estimate to perform wiener filtering. In both steps the weighting scheme in the aggregation process plays an important role. The current weighting process depends on the variance of retrieved coefficients after denoising which results in a biased weighting. In PW-BM3D, we propose a novel probabilistic weighting scheme which is a function of the probability of similarity of noiseless patches in each 3D group. The results show improvement over BM3D in terms of PSNR for an average of about 0.2dB.

scholarly journals Image Zooming Based on Two Classes of C1-Continuous Coons Patches Construction with Shape Parameters over Triangular Domain

Symmetry ◽  
2020 ◽  
Vol 12 (4) ◽  
pp. 661 ◽  
Author(s):  
Yunyi Tang ◽  
Yuanpeng Zhu
Keyword(s):  
Resolution Enhancement ◽  
Sufficient Conditions ◽  
Wiener Filter ◽  
Similarity Index ◽  
Structural Similarity ◽  
Hermite Functions ◽  
Shape Parameters ◽  
Nonlocal Means ◽  
Triangular Domain ◽  
Image Zooming

Image interpolation is important in image zooming. To improve the quality of image zooming, in this work, we proposed a class of rational quadratic trigonometric Hermite functions with two shape parameters and two classes of C 1 -continuous Coons patches constructions over a triangular domain by improved side–side method and side–vertex method. Altering the values of shape parameters can adjust the interior shape of the triangular Coons patch without influencing the function values and partial derivatives of the boundaries. In order to deal with the problem of well-posedness in image zooming, we discussed symmetrical sufficient conditions for region control of shape parameters in the improved side–side method and side–vertex method. Some examples demonstrate the proposed methods are effective in surface design and digital image zooming. C 1 -continuous Coons patches constructed by the proposed methods can interpolate to scattered 3D data. By up-sampling to the constructed interpolation surface, high-resolution images can be obtained. Image zooming experiment and analysis show that compared to bilinear, bicubic, iterative curvature-based interpolation (ICBI), novel edge orientation adaptive interpolation scheme for resolution enhancement of still images (NEDI), super-resolution using iterative Wiener filter based on nonlocal means (SR-NLM) and rational ball cubic B-spline (RBC), the proposed method can improve peak signal to noise ratio (PSNR) and structural similarity index (SSIM). Edge detection using Prewitt operator shows that the proposed method can better preserve sharp edges and textures in image zooming. The proposed methods can also improve the visual effect of the image, therefore it is efficient in computation for image zooming.

scholarly journals Image Denoising in Spatial and Transform Domains

2021 ◽  
Author(s):  
Mina Sharifymoghaddam
Keyword(s):  
Image Denoising ◽  
Signal To Noise Ratio ◽  
Similarity Index ◽  
Weighting Scheme ◽  
Block Matching ◽  
Hard Thresholding ◽  
Weighted Version ◽  
Nonlocal Means ◽  
Processing Step ◽  
Index Measure

Image denoising is an inseparable pre-processing step of many image processing algorithms. Two mostly used image denoising algorithms are Nonlocal Means (NLM) and Block Matching and 3D Transform Domain Collaborative Filtering (BM3D). While BM3D outperforms NLM on variety of natural images, NLM is usually preferred when the algorithm complexity is an issue. In this thesis, we suggest modified version of these two methods that improve the performance of the original approaches. The conventional NLM uses weighted version of all patches in a search neighbourhood to denoise the center patch. However, it can include some dissimilar patches. Our first contribution, denoted by Similarity Validation Based Nonlocal Means (NLM-SVB), eliminates some of those unnecessary dissimilar patches in order to improve the performance of the algorithm. We propose a hard thresholding pre-processing step based on the exact distribution of distances of similar patches. Consequently, our method eliminates about 60% of dissimilar patches and improves NLM in terms of Peak Signal to Noise Ratio (PSNR) and Stracuteral Similarity Index Measure (SSIM). Our second contribution, denoted by Probabilistic Weighting BM3D (PW-BM3D), is the result of our thorough study of BM3D. BM3D consists of two main steps. One is finding a basic estimate of the noiseless image by hard thresholding coefficients. The second one is using this estimate to perform wiener filtering. In both steps the weighting scheme in the aggregation process plays an important role. The current weighting process depends on the variance of retrieved coefficients after denoising which results in a biased weighting. In PW-BM3D, we propose a novel probabilistic weighting scheme which is a function of the probability of similarity of noiseless patches in each 3D group. The results show improvement over BM3D in terms of PSNR for an average of about 0.2dB.

A Natural Image Compression Approach Based on ICA and Visual Saliency Detection

2011 ◽  
Vol 128-129 ◽  
pp. 457-460
Author(s):  
Li Juan Duan ◽  
Chun Xia Ke ◽  
Chun Peng Wu ◽  
Zhen Yang ◽  
Jun Miao
Keyword(s):  
Image Compression ◽  
Saliency Detection ◽  
Similarity Index ◽  
Visual Saliency ◽  
Structural Similarity ◽  
Natural Image ◽  
Compression Method ◽  
Method Performance ◽  
Important Region ◽  
Visual Saliency Detection

In this paper, a natural image compression method is proposed based on independent component analysis (ICA) and visual saliency detection. The proposed compression method learns basis functions trained from data using ICA to transform the image at first; and then sets percentage of the zero coefficient number in the total transforming coefficients. After that, transforming coefficients are sparser which indicates further improving of compression ratio. Next, the compression method performance is compared with the discrete cosine transform (DCT). Evaluation through both the usual PSNR and Structural Similarity Index (SSIM) measurements showed that proposed compression method is more robust to DCT. And finally, we proposed a visual saliency detection method to detect automatically the important region of image which is not or low compressed while the other regions are highly compressed. Experiment shows that the method can guarantee the quality of important region effectively.

scholarly journals COMPARISON OF IMAGE ENHANCEMENT METHODS FOR CHROMOSOME KARYOTYPE IMAGE ENHANCEMENT

2017 ◽  
Vol 10 (1) ◽  
pp. 50 ◽  
Author(s):  
Dewa Made Sri Arsa ◽  
Grafika Jati ◽  
Agung Santoso ◽  
Rafli Filano ◽  
Nurul Hanifah ◽  
...  
Keyword(s):  
Image Enhancement ◽  
Signal To Noise Ratio ◽  
Similarity Index ◽  
Structural Similarity ◽  
Histogram Equalization ◽  
Block Matching ◽  
Structural Similarity Index ◽  
Method Performance ◽  
Enhancement Method ◽  
Basic Image

The chromosome is a set of DNA structure that carry information about our life. The information can be obtained through Karyotyping. The process requires a clear image so the chromosome can be evaluate well. Preprocessing have to be done on chromosome images that is image enhancement. The process starts with image background removing. The image will be cleaned background color. The next step is image enhancement. This paper compares several methods for image enhancement. We evaluate some method in image enhancement like Histogram Equalization (HE), Contrast-limiting Adaptive Histogram Equalization (CLAHE), Histogram Equalization with 3D Block Matching (HE+BM3D), and basic image enhancement, unsharp masking. We examine and discuss the best method for enhancing chromosome image. Therefore, to evaluate the methods, the original image was manipulated by the addition of some noise and blur. Peak Signal-to-noise Ratio (PSNR) and Structural Similarity Index (SSIM) are used to examine method performance. The output of enhancement method will be compared with result of Professional software for karyotyping analysis named Ikaros MetasystemT M . Based on experimental results, HE+BM3D method gets a stable result on both scenario noised and blur image. 

scholarly journals Adaptively Tuned Iterative Low Dose CT Image Denoising

2015 ◽  
Vol 2015 ◽  
pp. 1-12 ◽  
Author(s):  
SayedMasoud Hashemi ◽  
Narinder S. Paul ◽  
Soosan Beheshti ◽  
Richard S. C. Cobbold
Keyword(s):  
Image Denoising ◽  
Low Dose ◽  
Ad Hoc ◽  
Confidence Region ◽  
Similarity Index ◽  
Structural Similarity ◽  
Block Matching ◽  
Ct Image ◽  
Denoising Method ◽  
Low Dose Ct

Improving image quality is a critical objective in low dose computed tomography (CT) imaging and is the primary focus of CT image denoising. State-of-the-art CT denoising algorithms are mainly based on iterative minimization of an objective function, in which the performance is controlled by regularization parameters. To achieve the best results, these should be chosen carefully. However, the parameter selection is typically performed in an ad hoc manner, which can cause the algorithms to converge slowly or become trapped in a local minimum. To overcome these issues a noise confidence region evaluation (NCRE) method is used, which evaluates the denoising residuals iteratively and compares their statistics with those produced by additive noise. It then updates the parameters at the end of each iteration to achieve a better match to the noise statistics. By combining NCRE with the fundamentals of block matching and 3D filtering (BM3D) approach, a new iterative CT image denoising method is proposed. It is shown that this new denoising method improves the BM3D performance in terms of both the mean square error and a structural similarity index. Moreover, simulations and patient results show that this method preserves the clinically important details of low dose CT images together with a substantial noise reduction.

scholarly journals Edge-aware image denoising algorithm

2018 ◽  
Vol 13 ◽  
pp. 174830181880477
Author(s):  
Xiangning Zhang ◽  
Yan Yang ◽  
Lening Lin
Keyword(s):  
Image Denoising ◽  
Information Needs ◽  
Signal To Noise Ratio ◽  
Structural Similarity ◽  
Block Matching ◽  
External Information ◽  
Noisy Image ◽  
Original Image ◽  
Similar Images ◽  
Edge Features

The key of image denoising algorithms is to preserve the details of the original image while denoising the noise in the image. The existing algorithms use the external information to better preserve the details of the image, but the use of external information needs the support of similar images or image patches. In this paper, an edge-aware image denoising algorithm is proposed to achieve the goal of preserving the details of original image while denoising and using only the characteristics of the noisy image. In general, image denoising algorithms use the noise prior to set parameters todenoise the noisy image. In this paper, it is found that the details of original image can be better preserved by combining the prior information of noise and the image edge features to set denoising parameters. The experimental results show that the proposed edge-aware image denoising algorithm can effectively improve the performance of block-matching and 3D filtering and patch group prior-based denoising algorithms and obtain higher peak signal-to-noise ratio and structural similarity values.

scholarly journals Image Text Deblurring Method Based on Generative Adversarial Network

Electronics ◽  
2020 ◽  
Vol 9 (2) ◽  
pp. 220
Author(s):  
Chunxue Wu ◽  
Haiyan Du ◽  
Qunhui Wu ◽  
Sheng Zhang
Keyword(s):  
Similarity Index ◽  
Human Perception ◽  
Specific Area ◽  
Structural Similarity ◽  
Wasserstein Distance ◽  
Generative Adversarial Networks ◽  
Natural Image ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Adversarial Networks

In the automatic sorting process of express delivery, a three-segment code is used to represent a specific area assigned by a specific delivery person. In the process of obtaining the courier order information, the camera is affected by factors such as light, noise, and subject shake, which will cause the information on the courier order to be blurred, and some information will be lost. Therefore, this paper proposes an image text deblurring method based on a generative adversarial network. The model of the algorithm consists of two generative adversarial networks, combined with Wasserstein distance, using a combination of adversarial loss and perceptual loss on unpaired datasets to train the network model to restore the captured blurred images into clear and natural image. Compared with the traditional method, the advantage of this method is that the loss function between the input and output images can be calculated indirectly through the positive and negative generative adversarial networks. The Wasserstein distance can achieve a more stable training process and a more realistic generation effect. The constraints of adversarial loss and perceptual loss make the model capable of training on unpaired datasets. The experimental results on the GOPRO test dataset and the self-built unpaired dataset showed that the two indicators, peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM), increased by 13.3% and 3%, respectively. The human perception test results demonstrated that the algorithm proposed in this paper was better than the traditional blur algorithm as the deblurring effect was better.

scholarly journals Adaptive Weighted High Frequency Iterative Algorithm for Fractional-Order Total Variation with Nonlocal Regularization for Image Reconstruction

Electronics ◽  
2020 ◽  
Vol 9 (7) ◽  
pp. 1103
Author(s):  
Hui Chen ◽  
Yali Qin ◽  
Hongliang Ren ◽  
Liping Chang ◽  
Yingtian Hu ◽  
...  
Keyword(s):  
Total Variation ◽  
Iterative Algorithm ◽  
Fractional Order ◽  
High Frequency ◽  
Structural Information ◽  
Similarity Index ◽  
Structural Similarity ◽  
Nonlocal Means ◽  
Nonlocal Regularization ◽  
Staircase Artifacts

We propose an adaptive weighted high frequency iterative algorithm for a fractional-order total variation (FrTV) approach with nonlocal regularization to alleviate image deterioration and to eliminate staircase artifacts, which result from the total variation (TV) method. The high frequency gradients are reweighted in iterations adaptively when we decompose the image into high and low frequency components using the pre-processing technique. The nonlocal regularization is introduced into our method based on nonlocal means (NLM) filtering, which contains prior image structural information to suppress staircase artifacts. An alternating direction multiplier method (ADMM) is used to solve the problem combining reweighted FrTV and nonlocal regularization. Experimental results show that both the peak signal-to-noise ratios (PSNR) and structural similarity index (SSIM) of reconstructed images are higher than those achieved by the other four methods at various sampling ratios less than 25%. At 5% sampling ratios, the gains of PSNR and SSIM are up to 1.63 dB and 0.0114 from ten images compared with reweighted total variation with nuclear norm regularization (RTV-NNR). The improved approach preserves more texture details and has better visual effects, especially at low sampling ratios, at the cost of taking more time.

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